Transfer learning for radio galaxy classification

In the context of radio galaxy classification, most state-of-the-art neural network algorithms have been focused on single survey data. The question of whether these trained algorithms have cross-survey identification ability or can be adapted to develop classification networks for future surveys is still unclear. One possible solution to address this issue is transfer learning, which re-uses elements of existing machine learning models for different applications. Here we present radio galaxy classification based on a 13-layer Deep Convolutional Neural Network (DCNN) using transfer learning methods between different radio surveys. We find that our machine learning models trained from a random initialization achieve accuracies comparable to those found elsewhere in the literature. When using transfer learning methods, we find that inheriting model weights pre-trained on FIRST images can boost model performance when re-training on lower resolution NVSS data, but that inheriting pre-trained model weights from NVSS and re-training on FIRST data impairs the performance of the classifier. We consider the implication of these results in the context of future radio surveys planned for next-generation radio telescopes such as ASKAP, MeerKAT, and SKA1-MID

Testing protoplanetary disc dispersal with radio emission

We consider continuum free-free radio emission from the upper atmosphere of protoplanetary discs as a probe of the ionized luminosity impinging upon the disc. Making use of previously computed hydrodynamic models of disc photoevaporation within the framework of EUV and X-ray irradiation, we use radiative transfer post-processing techniques to predict the expected free-free emission from protoplanetary discs. In general, the free-free luminosity scales roughly linearly with ionizing luminosity in both EUV and X-ray driven scenarios, where the emission dominates over the dust tail of the disc and is partial optically thin at cm wavelengths. We perform a test observation of GM Aur at 14-18 Ghz and detect an excess of radio emission above the dust tail to a very high level of confidence. The observed flux density and spectral index are consistent with free-free emission from the ionized disc in either the EUV or X-ray driven scenario. Finally, we suggest a possible route to testing the EUV and X-ray driven dispersal model of protoplanetary discs, by combining observed free-free flux densities with measurements of mass-accretion rates. On the point of disc dispersal one would expect to find a M_dot^2 scaling with free-free flux in the case of EUV driven disc dispersal or a M_dot scaling in the case of X-ray driven disc dispersal.Comment: Accepted MNRAS, 12 pages, 11 figures, (pdf generation fixed

MCMC to address model misspecification in Deep Learning classification of Radio Galaxies

The radio astronomy community is adopting deep learning techniques to deal with the huge data volumes expected from the next-generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model uncertainty in the predictions made by deep learning models and will play an important role in extracting well-calibrated uncertainty estimates from the outputs of these models. However, most commonly used approximate Bayesian inference techniques such as variational inference and MCMC-based algorithms experience a "cold posterior effect (CPE)", according to which the posterior must be down-weighted in order to get good predictive performance. The CPE has been linked to several factors such as data augmentation or dataset curation leading to a misspecified likelihood and prior misspecification. In this work we use MCMC sampling to show that a Gaussian parametric family is a poor variational approximation to the true posterior and gives rise to the CPE previously observed in morphological classification of radio galaxies using variational inference based BNNs.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 1 figure, 1 tabl

Limits on the validity of the thin-layer model of the ionosphere for radio interferometric calibration

For a ground-based radio interferometer observing at low frequencies, the ionosphere causes propagation delays and refraction of cosmic radio waves which result in phase errors in the received signal. These phase errors can be corrected using a calibration method that assumes a two-dimensional phase screen at a fixed altitude above the surface of the Earth, known as the thin-layer model. Here we investigate the validity of the thin-layer model and provide a simple equation with which users can check when this approximation can be applied to observations for varying time of day, zenith angle, interferometer latitude, baseline length, ionospheric electron content and observing frequency.Comment: 8 pages, 10 figures, accepted MNRA

Efficient Source Finding for Radio Interferometric Images

Object detection in astronomical images, generically referred to as source finding, is often performed before the object characterisation stage in astrophysical processing work flows. In radio astronomy, source finding has historically been performed by bespoke off-line systems; however, modern data acquisition systems as well as those proposed for upcoming observatories such as the Square Kilometre Array (SKA), will make this approach unfeasible. One area where a change of approach is particularly necessary is in the design of fast imaging systems for transient studies. This paper presents a number of advances in accelerating and automating the source finding in such systems.Comment: submitted to Astronomy & Computin

Sub-arcsecond high sensitivity measurements of the DG~Tau jet with e-MERLIN

We present very high spatial resolution deep radio continuum observations at 5 GHz (6 cm) made with e-MERLIN of the young stars DG Tau A and B. Assuming it is launched very close (~=1 au) from the star, our results suggest that the DG Tau A outflow initially starts as a poorly focused wind and undergoes significant collimation further along the jet (~=50 au). We derive jet parameters for DG Tau A and find an initial jet opening angle of 86 degrees within 2 au of the source, a mass-loss rate of 1.5x10^-8 solar masses/yr for the ionised component of the jet, and the total ejection/accretion ratio to range from 0.06-0.3. These results are in line with predictions from MHD jet-launching theories.Comment: Accepted MNRAS Letter

Combining astrophysical datasets with CRUMB

At present, the field of astronomical machine learning lacks widely-used benchmarking datasets; most research employs custom-made datasets which are often not publicly released, making comparisons between models difficult. In this paper we present CRUMB, a publicly-available image dataset of Fanaroff-Riley galaxies constructed from four "parent" datasets extant in the literature. In addition to providing the largest image dataset of these galaxies, CRUMB uses a two-tier labelling system: a "basic" label for classification and a "complete" label which provides the original class labels used in the four parent datasets, allowing for disagreements in an image's class between different datasets to be preserved and selective access to sources from any desired combination of the parent datasets.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 1 figure, 1 tabl